Scalable computing systems for generating notifications, and methods of operating thereof

ABSTRACT

A scalable computing system for generating notifications for each account of a plurality of accounts, and method of operating thereof, are provided. The system involves: a database for storing: an action definition defining operations executable to determine at least one parameter of one or more actions, and a processor operable to: instantiate an independent management process for the action definition, wherein each management process is configured to: monitor a state of each operation defined in the action definition; identify a worker process associated with the management process to perform each operation based on the state of the operation; and assign the identified worker process to perform the respective operation; and upon detecting a predefined condition in each independent management process, transmit, via a network to a remote node, the one or more notifications for the respective independent management process.

FIELD

The described embodiments relate to a scalable computing system forgenerating notifications, and methods of operating thereof.

BACKGROUND

Computing systems are frequently used to speed batch processing offunctions and calculations. However, in many cases, the amount of datato be processed may grow at a rate that exceeds the ability of thecomputing system to scale. To address the growing load requirements,additional physical resources are typically required. However, in somecases, the addition of physical resources may not be sufficient tomaintain a performance level of a computing system due to its designlimitations.

SUMMARY

The various embodiments described herein generally relate to scalablecomputing systems for generating one or more notifications for each of aplurality of accounts, and methods of operating thereof.

In accordance with some embodiments, there is provided a scalablecomputing system including: a database for storing: an action definitiondefining one or more operations executable to determine at least oneparameter of one or more actions; and a processor in communication withthe database, the processor being operable to: instantiate anindependent management process for the action definition of each accountof the plurality of accounts managed by the scalable computing system,wherein each management process is configured to: monitor a state ofeach operation defined in the action definition; identify a workerprocess associated with the management process to perform each operationbased on the respective state of the operation; and assign theidentified worker process to perform the respective operation; and upondetecting a predefined condition in each independent management process,transmit, via a network to a remote node, the one or more notificationsfor the respective independent management process.

In some embodiments, the management process is configured to: determinethe state of each operation defined in the respective action definition;and identify an operation with an incomplete status.

In some embodiments, the management process is configured to: determinea load parameter of each operation, the load parameter indicating aprocessing capacity required for performing the respective operation;identify one or more worker processes capable of satisfying the loadparameter; and select a worker process from the identified one or moreworker processes for performing the operation.

In some embodiments, the management process is configured to: determinea machine type associated with the load parameter; and identify the oneor more worker processes available at a machine associated with themachine type.

In some embodiments, the management process is configured to: determinewhether an operating capacity of the identified one or more workerprocesses exceeds a capacity threshold; and in response to determiningthe operating capacity exceeds the capacity threshold, delay theperformance of the operation until the operating capacity does notexceed the capacity threshold.

In some embodiments, to delay the performance of the operation, themanagement process is configured to: assign a wait state to thatoperation.

In some embodiments, the one or more operations defined by the actiondefinition involves an notification generator operation; and themanagement process is configured to identify the worker process toperform the notification generator operation, wherein the notificationgenerator operation includes: receiving one or more parameters for theone or more notifications from at least one other operation defined bythe action definition; and generating the one or more notificationsbased on the one or more parameters.

In some embodiments, the one or more operations defined by the actiondefinition involves at least one of a risk profile determinationoperation, a portfolio determination operation, a position determinationoperation, a fee determination operation, an account statusdetermination operation, and an order submission operation.

In some embodiments, the processor is operable to instantiate two ormore management processes, each management process being associated witha different set of worker processes.

In some embodiments, the management process is associated with one ormore worker processes selectable by the management process forperforming a respective operation defined by the action definition.

In some embodiments, the independent management process is configured toimplement a state machine for the respective action definition.

In accordance with some embodiments, there is provided a method ofoperating a scalable computing system to generate one or morenotifications for each of a plurality of accounts. The method includes:operating a processor to instantiate an independent management processfor an action definition of each account of the plurality of accountsmanaged by a scalable computing system, the action definition definingone or more operations executable to determine at least one parameter ofone or more actions, wherein each management process is configured to:monitor a state of each operation defined in the action definition;identify a worker process associated with the management process toperform each operation based on the respective state of the operation;and assign the identified worker process to perform the respectiveoperation; and operating the processor to, upon detecting a predefinedcondition in each independent management process, transmit, via anetwork to a remote node, the one or more notifications for therespective independent management process.

In some embodiments, the management process is configured to: determinethe state of each operation defined in the respective action definition;and identify an operation with an incomplete status.

In some embodiments, the management process is configured to: determinea load parameter of each operation, the load parameter indicating aprocessing capacity required for performing the respective operation;identify one or more worker processes capable of satisfying the loadparameter; and select a worker process from the identified one or moreworker processes for performing the operation.

In some embodiments, the management process is configured to: determinea machine type associated with the load parameter; and identify the oneor more worker processes available at a machine associated with themachine type.

In some embodiments, the management process is configured to: determinewhether an operating capacity of the identified one or more workerprocesses exceeds a capacity threshold; and in response to determiningthe operating capacity exceeds the capacity threshold, delay theperformance of the operation until the operating capacity does notexceed the capacity threshold.

In some embodiments, to delay the performance of the operation, themanagement process is configured to: assign a wait state to thatoperation.

In some embodiments, the one or more operations defined by the actiondefinition includes an notification generator operation; and themanagement process is configured to identify the worker process toperform the notification generator operation, wherein the notificationgenerator operation includes: receiving one or more parameters for theone or more notifications from at least one other operation defined bythe action definition; and generating the one or more notificationsbased on the one or more parameters.

In some embodiments, the one or more operations defined by the actiondefinition includes at least one of a risk profile determinationoperation, a portfolio determination operation, a position determinationoperation, a fee determination operation, an account statusdetermination operation, and an order submission operation.

In some embodiments, the methods disclosed herein include operating theprocessor to instantiate two or more management processes, eachmanagement process being associated with a different set of workerprocesses.

In some embodiments, the management process is associated with one ormore worker processes selectable by the management process forperforming a respective operation defined by the action definition.

In some embodiments, the independent management process is configured toimplement a state machine for the respective action definition.

In some embodiments, the methods disclosed herein include storing theaction definition in a database accessible by the processor.

BRIEF DESCRIPTION OF THE DRAWINGS

Several embodiments will now be described in detail with reference tothe drawings, in which:

FIG. 1 is a block diagram of components interacting with an examplescalable computing system in accordance with an example embodiment;

FIG. 2 is a flowchart of an example embodiment of various methods ofoperating a scalable computing system;

FIG. 3 is an example data flow diagram in accordance with an exampleembodiment;

FIG. 4 is an example state machine diagram for an order generationservice in accordance with an example embodiment;

FIG. 5 is an example state machine diagram for a withdrawal service inaccordance with an example embodiment and

FIG. 6 is an example state machine diagram for a manufacturing processin accordance with an example embodiment.

The drawings, described below, are provided for purposes ofillustration, and not of limitation, of the aspects and features ofvarious examples of embodiments described herein. For simplicity andclarity of illustration, elements shown in the drawings have notnecessarily been drawn to scale. The dimensions of some of the elementsmay be exaggerated relative to other elements for clarity. It will beappreciated that for simplicity and clarity of illustration, whereconsidered appropriate, reference numerals may be repeated among thedrawings to indicate corresponding or analogous elements or steps.

DESCRIPTION OF EXAMPLE EMBODIMENTS

The various embodiments described herein generally relate to scalablecomputing systems for generating one or more notifications (andassociated methods for providing the systems).

Computing systems are designed to satisfy various requirements, such asload capacity and performance speed. When those computing systems needto process a larger amount of data, their scalability can be limited bythe original design and/or hardware scalability. For example, when acomputing system operates based on a single process, it may be difficultto adapt that computing system for parallel processing (e.g.,multi-threaded processes). Instead, to adapt that example computingsystem for larger processing loads, higher performing hardware resourcesare likely required.

In a computing system for automatically performing trades, for example,the computing system can be designed to automatically evaluate and tradean initial number of client accounts according to a single process. Whenthe number of client accounts grows, it can be difficult to scale thisautomatic trading system in a way that can maintain the originallydesign performance. The addition of higher performance hardwareresources can be one way to scale the automatic trading system but thehardware resources also come with procurement and maintenance costs, andother issues.

Similarly, a computing system that monitors and manages a manufacturingprocess, for example, can be difficult to adapt to variation in dataprocessing needs. Typical computing systems designed for a manufacturingprocess are linear in design and any addition to the load can severelyreduce the processing speed. The designs of these computing systems tendto be rigid and so, any variations in the manufacturing process mayrequire a complete redesign of the computing systems.

Also, the design of computing systems typically considers specificfactors related to the application of that computing system. Forexample, in the context of an automatic trading system, the differenttime zones of the stock markets can limit the amount of time availablefor the trades to be evaluated and traded. Also, depending on the regionassociated with a client account, different regulations may apply, whichmust be considered by the trade evaluation process. For example, whenthe automatic trading system considers tax-loss harvesting (TLH) inCanada, the system will typically only require data from the last 30days, but in the United States, the automatic trading system willrequire data from a full calendar year due to American short-term andlong-term capital gains tax rules.

The described embodiments generally provide systems and methods forscalable computing. The scalable computing may be provided by carryingout a state-machine “workflow”. The workflow may be implemented by aplurality of independent workflow instances. Each workflow instance canbe, paused, resumed, cancelled or restarted without affecting otherinstances. Each workflow instance can run in parallel with otherinstances.

Scaling of workflow instances can be accomplished by several techniques.A first technique is to provide a collection of micro services forcarrying out each step of a workflow. Each micro service can be writtenwith a desired technology and language to carry out a discrete action.In a cloud-based environment, these different micro services can beprovisioned onto hardware optimized for the specific tasks theyaccomplish.

For example, a computationally heavy action can be deployed onto amachine with a high performing central processing unit (CPU). Each ofthese micro services can scale and provision additional machines tocomplete tasks in parallel at a faster rate.

A second technique is to subdivide the state machine component into amanagement process and worker processes. The management processgenerally keeps track of all the tasks to be completed and assigns tasksto available worker processes. The worker processes take the tasks andexecute the tasks using the various micro services described herein.Additional groups of management process and associated worker processescan be provisioned to handle more tasks. The management process can alsoload-balance and delay the execution of certain tasks to avoidoverloading the underlying micro services.

A further technique is to partition source data across differentdatabases and services. Accessing large volumes of data can createbottlenecks when multiple processes attempt to retrieve data from aparticular database or service. Partitioning data into multipledatabases and services allows for workflows to work off completelyisolated data sets, thus avoiding the problem of a large bottleneck.

Reference is now made to FIG. 1, which illustrates a block diagram 100of components interacting with an example scalable computing system 110.The scalable computing system 110 can be in communication with at leastone computing device 140, a remote node 130 and a system database 120via a network 150.

The computing devices 140 may be any networked device operable toconnect to the network 150. A networked device may couple to the network150 through a wired or wireless connection.

The computing device 140 may include at least a processor and memory,and may be an electronic tablet device, a personal computer,workstation, server, portable computer, mobile device, personal digitalassistant, laptop, smart phone, WAP phone, an interactive television,video display terminals, gaming consoles, and portable electronicdevices or any combination of these.

The network 150 may be any network capable of carrying data, includingthe Internet, Ethernet, plain old telephone service (POTS) line, publicswitch telephone network (PSTN), integrated services digital network(ISDN), digital subscriber line (DSL), coaxial cable, fiber optics,satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network,fixed line, local area network, wide area network, and others, includingany combination of these, capable of interfacing with, and enablingcommunication between the computing device 140, the remote node 130, thesystem database 120 and the scalable computing system 110.

The scalable computing system 110, the computing device 140, the remotenode 130 and the system database 120 may connect to the network 150 or aportion thereof via suitable network interfaces. For example, thecomputing device 140 can include a connected or “smart” device capableof data communication, such as thermostats, air quality sensors,industrial equipment and the like.

The remote node 130 can include networked equipment, asoftware-as-a-service server (SaaS server) and/or other computing system(with at least a processor, a memory and a network interface) forreceiving the notifications generated by the scalable computing system110. In some embodiments, the remote node 130 can include one or morecomputing systems operable by the scalable computing system 110 toperform some or all of an operation defined within the actiondefinition. Example computing systems are described with reference toFIG. 3. It should be understood that although only one remote node 130is shown, there may be multiple remote nodes 130 distributed over a widegeographic area and connected via the network 150.

An example networked equipment can include an industrial machine,facilities equipment, sensor, or any other machine that can be connectedto the network 150. The networked equipment can include a processor,such as a microcontroller, a memory that may include volatile andnon-volatile elements, and at least one network interface. The networkedequipment may include additional input or output devices, in someembodiments.

Like the networked equipment, the SaaS server has a processor, volatileand non-volatile memory, at least one network interface, and may havevarious other input/output devices. In many cases, the SaaS server maybe constructed from a server farm, which may be in geographicallydiverse locations, and accessed via a load balancer. Such arrangementsare sometimes referred to as “cloud” services. In general, the SaaSserver provides one or more software applications, and may be accessedby one or more device from within the network 150 and occasionally fromoutside of network 150.

As used herein, the term “software application” or “application” refersto computer-executable instructions, particularly computer-executableinstructions stored in a non-transitory medium, such as a non-volatilememory, and executed by a processor. The processor, when executing theinstructions, may receive inputs and transmit outputs to any of avariety of input or output devices to which it is coupled. Within anorganization, a software application may be recognized by a name by boththe people who use it, and those that supply or maintain it. A softwareapplication can be, for example, a monolithic software application,built in-house by the organization and possibly running on customhardware; a set of interconnected modular subsystems running on similaror diverse hardware; a software-as-a-service application operatedremotely by a third party; third party software running on outsourcedinfrastructure, etc. In some cases, a software application also may beless formal, or constructed in ad hoc fashion, such as a programmablespreadsheet document that has been modified to perform computations forthe organization's needs. For example, for many organizations, importantapplications and services rely on regular input from spreadsheets thatmay be obtained from third parties, so these spreadsheets may beidentified as software applications.

The system database 120 can act as a back-up database for the datastored in the memory 114 of the scalable computing system 110, and/orstoring data that may not be as frequently used.

The scalable computing system 110 includes a processor 112, an interfacecomponent 118 and a memory 114. The processor 112, the interfacecomponent 118 and the memory 114 can communicate with each other. Thememory 114, as shown, can include one or more databases 116. In FIG. 1,the scalable computing system 110 is shown as one element for ease ofexposition. It should be understood that there may be multiple scalablecomputing systems 110 distributed over a wide geographic area andconnected via the network 150.

It will be understood that, in some embodiments, each of the processor112, the interface component 118, and the memory 114 may be combinedinto a fewer number of components or may be separated into furthercomponents. Furthermore, the processor 112, the interface component 118,and the memory 114 may be implemented in software or hardware, or acombination of software and hardware.

The processor 112 may be any suitable processors, controllers or digitalsignal processors that can provide sufficient processing power dependingon the configuration, purposes and requirements of the scalablecomputing system 110. In some embodiments, the processor 112 can includemore than one processor with each processor being configured to performdifferent dedicated tasks.

The processor 112 can be configured to control the operation of thescalable computing system 110. The processor 112 can initiate and managethe operations of each of the interface component 118 and the memory114.

The interface component 118 may be any interface that enables thescalable computing system 110 to communicate with other devices andsystems. In some embodiments, the interface component 118 can include atleast one of a serial port, a parallel port or a USB port. The interfacecomponent 118 may also include at least one of an Internet, Local AreaNetwork (LAN), Ethernet, Firewire, modem or digital subscriber lineconnection. Various combinations of these elements may be incorporatedwithin the interface component 118.

For example, the interface component 118 may receive input from variousinput devices, such as a mouse, a keyboard, a touch screen, athumbwheel, a track-pad, a track-ball, a card-reader, voice recognitionsoftware and the like depending on the requirements and implementationof the scalable computing system 110.

The memory 114 can include RAM, ROM, one or more hard drives, one ormore flash drives or some other suitable data storage elements such asdisk drives, etc. The memory 114 can be used to store an operatingsystem and software applications. For instance, the operating system canprovide various basic operational processes. The programs can includevarious user programs so that a user can interact with the interfacecomponent 118 to perform various functions such as, but not limited to,viewing and/or responding to the notifications generated by the scalablecomputing system 110.

The memory 114 can store data related to the accounts associated withthe scalable computing system 110, for example. The accounts can relateto a user, and/or a specific product or project whose status is beingmonitored by the scalable computing system 110. For example, an accountcan be associated with a product being manufactured in an assembly line,a project whose status is being monitored, or an investment accountbeing automatically evaluated for trades.

An example method of operating the scalable computing system 110 willnow be described with reference to FIGS. 2 and 3. FIG. 2 is a flowchartof an example method 200 of operating the scalable computing system 110.FIG. 3 illustrates an example data flow diagram 300 for the scalablecomputing system 110.

For each account, the scalable computing system 110 can operateaccording to an action definition to generate notifications for thataccount.

The action definition defines operations executable to determine atleast one parameter of the notifications to be generated. The actiondefinition can also define the variables that are required by the remotecomputing systems 340 triggered by a worker process 314, and/or theresponses that should be stored in the database 116/120. The actiondefinition can be stored in the database 116 and/or 120, for example.

Each operation defined in the action definition is responsible for a setof behaviours and/or actions that form the parameters of thenotifications. For example, the operations can involve gathering and/orprocessing data from the remote computing systems 340 and/or triggeringthe remote computing systems 340 to gather and/or process specific data.The architecture of each remote computing systems 340 can be based on acollection of coupled services, such as, but not limited to aservice-oriented architecture (SOA) or a micro services architecture.The services available at the remote computing systems 340 can betriggered to perform the actions needed for the respective operation.

One example action definition for a trading environment is provided inTable 1, below. The example action definition is provided using theJavascript Object Notation (JSON) format. However, other formats may beused as desired.

TABLE 1 Example action definition {  “name”: “trading_workflow”, “initial_context”: [   “account_ids”  ],  “start_state”: “get_name”, “states”: [   {    “service”: “profile_service”,    “action”:“get_risk_profiles”,    “response_data”: [     “investment_profiles”   ],    “transitions”: {     “success”: “get_model_portfolios”    }  },   {    “service”: “portfolio_service”,    “action”:“get_model_portfolios”,    “request_data”: [     “account_ids”,    “investment_profiles”    ],    “response_data”: [    “target_portfolios”    ],    “transitions”: {     “success”:“get_account_states”    }   },   {    “service”: “portfolio_service”,   “action”: “get_account_states”,    “request_data”: [    “account_ids”    ],    “response_data”: [     “account_states”    ],   “transitions”: {     “success”: “get_positions”    }   },   {   “service”: “books_service”,    “action”: “get_positions”,   “request_data”: [     “account_ids”    ],    “response_data”: [    “positions”    ],    “transitions”: {     “success”:“get_fee_structure”    }   },   {    “service”: “fee_service”,   “action”: “get_fee_structure”,    “request_data”: [    “account_ids”,     “target_portfolios”    ],    “response_data”: [    “fee_structures”    ],    “transitions”: {     “success”:“calculate_orders”    }   },   {    “service”: “order_calculator”,   “action”: “calculate_orders”,    “request_data”: [     “account_ids”,    “investment_profiles”,     “target_portfolios”,    “account_states”,     “positions”,     “fee_structures”    ],   “response_data”: [     “orders”    ],    “transitions”: {    “success”: “submit_orders”    }   },   {    “service”:“submit_orders”,    “action”: “calculate_orders”,    “request_data”: [    “orders”    ],    “transitions”: {     “success”: “done”    }   },  {    “state_name”: “done”,    “terminal”: true   }  ] }

As will be described, an operation in an example application of thescalable computing system 110 for an automatic trading environment caninclude triggering a remote computing system 340 to gather data relatedto the accounts, calculate one or more orders, generate orders,determine a risk profile, determine a portfolio, determine a positiondetermination, determine a fee, determine an account status, and submitan order. Another example scalable computing system 110 can be in themanufacturing process. Example operations in the manufacturing processcan include triggering a remote computing system 340 to receive thesupplies required to manufacture the product, an availability status ofcomponents required to manufacture a product, a readiness for shippingof the product, etc. In some embodiments, the operations can include anotification generator operation.

The notifications can include, but is not limited to, alerts that can bedisplayed via the remote node 130 for a user for review, and/or anindication of a status of the account being monitored. An example remoteuser node 330 is shown in FIG. 3. The example remote user node 330includes a database 332 and a user interface 334. It will be understoodthat other implementations of the remote user node 330 can be used.

Each operation can be triggered by a worker process 314 to be executedat a remote computing system 340. In some embodiments, the scalablecomputing system 110 can select remote computing systems 340 that haveresources appropriate for the execution of the operation. For example,operations that involve intense data processing can be assigned to aremote computing system 340 with high processing resources.

The operations associated with each account can be divided between theremote computing systems 340 a to 340 f. In the illustrated example,each remote computing system 340 a to 340 e includes respectivedatabases and applications (e.g., a collection of services), and remotecomputing system 340 f includes applications only. The remote computingsystems 340 a to 340 e can be suitable for operations involvingretrieving large amount of data, for example, while remote computingsystem 340 f can be assigned to computational-heavy operations. Althoughsix remote computing systems 340 are shown in FIG. 3, it will beunderstood that fewer or a greater number of remote computing systems340 can be used.

Each instance of the action definition operates independently fromanother instance and can run in parallel with other instances. Eachinstance of the action definition can be paused, resumed, cancelled,inspected, or restarted without affecting other instances. Any failuresthat may occur during the execution of an action definition for anaccount will not affect the operations being conducted for the otheraccounts.

Each remote computing system 340 can be defined using an externalresource definition, which may contain information regarding thecapabilities and constraints of each remote computing system 340. Forexample, the external resource definition may contain informationregarding how to authenticate with the remote computing system, whatprotocols may be used (e.g., PostgreSQL, HTTP, ZeroMQ, S3, GraphQL,etc.), or, optionally, information regarding resource availability(e.g., maximum load or connections). Depending on the protocoldefinition, an authentication attribute may be specified to indicate anauthentication method to be used (e.g., for HTTP, the authenticationattribute may indicate Basic, Digest or NTLM authentication).

As noted above, external resource definitions may be used to inform themanagement process how it may interact with other services. Externalresource definitions may be used to indicate resource limits that may beimposed by third party services operated by a different entity (e.g., athird party web server with an API that limits the maximum number ofconcurrent connections) and which may not scale in a predictable manner.

Examples of resource limits that may be specified in an externalresource definition may include a maximum amount of concurrentconnections, a maximum transaction size (e.g., defining how many itemscan be committed in one transaction), and a maximum request size (e.g.,defining how many items can be fetched in one request). For HTTP orURL-based services, additional route definitions may be provided for asubset of queries, such as a maximum request size, a maximum rate (e.g.,defining how many requests can be made within a predefined time period),a cache length (e.g., defining the period of time to store received datain order to avoid repeated requests for the same information), and acache size (e.g., defining how many data items to store beforediscarding items).

Table 2 illustrates an example external resource definition inaccordance with some embodiments.

TABLE 2 Example external resource definition “data_store”: { “protocol”: “postgresql”,  “url”: “https://data.example.com”, “username”: “<username>”,  “password”: “<password>”,  “config”: {  “max_connections”: 100,   “max_insert_size”: 10000,  “max_request_size”: 1000,  } }, “data_service”: {  “protocol”: “http”, “url”: “https://data.example.com”,  “auth”: “api-auth”,  “access_id”:“<access_id>”,  “secret_key”: “<secret_key”,  “config”: {  “max_connections”: 100,   “routes”: {    “default”: {     “default”: {     “max_request_size”: 250,     }    }   }  } },“third_party_service”: {  “protocol”: “http”,  “url”:“https://api.external.com”,  “auth”: “basic-auth”,  “username”:“<username>”,  “password”: “<password>” }, “brokerage_service”: {  “protocol”: “http”,   “url”: “https://api.brokerage.com”,   “auth”:“oauth2”,   “access_token”: “<access_token>”,   “refresh_token”:“<refresh_token>”,   “config”: {    “routes”: {     “/account/<param>”:{      “put”: {       “max_rate”: 1      },      “get”: {      “max_rate”: 30,       “max_request_size”: 15,      “cache_length”: 600,       “cache_size”: 100,      }     },    “default”: {      “default”: {       “max_rate”: 30,      “max_request_size”: 5,      }     }    }   } }

At 210, the processor 112 instantiates an independent management process312 for each action definition.

The management process 312 can be configured to implement a statemachine for the respective action definition. The management process 312is associated with one or more worker processes 314 a to 314 d thatexecute the operations assigned to them by the management process 312.Each action definition relates to an account.

When the processor 112 instantiates the management process 312 for theaction definition, the management process 312 can receive initial datarelated to the account and the action definition. The initial data caninclude an initial account data set and an initial state. Each operationdefined in the action definition is associated with various states, suchas in-operation state, incomplete state, wait state, or complete state.Based at least on the initial data and the action definition, themanagement process 312 can track the status of the operations defined inthe respective action definition with the state machine. The statemachine can provide a record of each state change. The records can bestored in the database 320 shown in FIG. 3, for example. The database320 can include the database 116 of the scalable computing system 110and/or the system database 120. Diagrams of example state machines willbe described with reference to FIGS. 4 to 6.

In some embodiments, the processor 112 can instantiate two or moremanagement processes 312. When the operations defined in the actiondefinition requires more resources, the processor 112 can be triggeredto instantiate more management processes 312 to handle those operations.Each management process 312 of the two or more management processes 312is associated with a different set of worker processes 314.

At 220, monitor, by the management process 312, a state of eachoperation defined in the action definition.

During the performance of the operations by the respective workerprocess 314, the worker process 314 can update a status of the operationin the database 320. The worker process 314 can also include in thedatabase 320 activity data, such as the data received and/or transmittedduring the performance of the operation.

The management process 312 can determine, from the database 116/120, thestate of each operation defined in the respective action definition. Themanagement process 312 can then identify an operation with an incompletestate, for example.

An operation can be assigned a wait state. For example, the managementprocess 312 can determine that the operation is time dependent and canlimit the availability of the performance of that operation to a latertime. The wait state can be used in situations when the scalablecomputing system 110 needs to be time zone aware. For example, in anautomatic trading environment, the stock markets start at different timezones and so, the scalable computing system 110 can designate theoperations that are affected by the variations in the time zones to takeplace at different times. In the manufacturing environment, the scalablecomputing system 110 can designate operations that require manualoversight to take place while workers are present, while operations thatdo not require manual oversight to take place while workers are notpresent.

At 230, identify, by the management process 312, a worker process 314associated with the management process 312 to perform each operationbased on the respective state of the operation.

The management process 312 assigns each worker process 314 to anoperation. The worker process 314 then performs the operation. Forexample, in a scalable computing system 110 for conducting automatictrading, an operation related to fetching positions is more limited toinput/output (I/O) operations. In contrast, an operation related toorder calculation requires more computing processing.

In some embodiments, as shown in FIG. 3, a worker process 314 can beimplemented with multiple threads. The multiple threads facilitateparallel computing at each of the worker processes 314. Although theworker processes 314 shown in FIG. 3 each show multiple threads, it willbe understood that there can be implementations of the worker processes314 without multiple threads and/or only one or more worker process 314with multiple threads.

The management process 312 can select at least one worker process 314 toperform an operation defined by the action definition. When selectingthe worker processes 314, the management process 312 can consider theload parameters of the operation and identify one or more workerprocesses 314 that can satisfy those load parameters. Load parametersgenerally indicate a processing capacity required for performing theoperation. From the identified worker processes 314 that can satisfythose load parameters, the management process 312 can select a workerprocess 314 from the identified worker processes 314 to perform theoperation.

In some embodiments, the management process 312 can determine a machinetype associated with the load parameter and select the worker process314 available at a machine associated with that machine type.

The management process 312 can also analyze the action definition andidentify any operations that do not depend on each other. The managementprocess can assign those independent operations to respective workerprocesses 314 that can perform those operations in parallel. Exampleindependent operations can include data gathering operations.

The management process 312 can also operate to balance the load asdefined by the action definition as between the worker processes 314.For example, the management process 312 can delay the execution of oneor more operations to avoid overloading the worker processes 314 and/orthe remote computing systems 340 that are required for the operation bythe worker processes 314. In some cases, the management process 312 mayalso focus or divert management or worker resources to other tasks orsteps to spread operating capacity.

In some embodiments, the management process 312 can determine whether anoperating capacity of the identified worker processes 314 exceeds acapacity threshold. If the operating capacity does exceed the capacitythreshold, the management process 312 can delay the performance of theoperation until the operating capacity does not exceed the capacitythreshold. To delay the performance of the operation, the managementprocess 312 can assign a wait state to that operation.

At 240, assign, by the management process 312, the identified workerprocess 314 to perform the respective operation.

When performing the operation, the worker process 314 can triggercommunication with the remote computing systems 340 to assist with theoperation. These remote computing systems 340 can be virtual machinescommunicating via the network 150. The remote computing systems 340 canalso scale to address the specific needs of the scalable computingsystem 110. For example, the remote computing systems 340 can be adaptedwith additional computing systems to enable the completion of theoperations in parallel and at a faster rate.

When accessing large volumes of data, bottlenecks can result when thedata is being retrieved from a database or from a particular remotecomputing system 340. With the described scalable computing system 110,the different instances of the management processes 312 for each actiondefinition enables the source data to be partitioned and split acrossdifferent databases or remote computing systems 340. In someembodiments, the instances of the management processes 312 can beconfigured to access isolated datasets to minimize bottleneck in thedata retrieval and/or data processing.

At least some of the data processing and/or gathering relevant to thegeneration of the notifications are shared among the remote computingsystems 340. As a result, when the scalable computing system 110 isfaced with a larger amount of data, the remote computing systems 340 canbe independently scaled. For example, when the operation assigned to aworker process 314 becomes more computationally intensive, the workerprocess 314 can be configured to trigger a remote computing system 340with a high performing central processing unit (CPU).

In some embodiments, a worker process 314 may require a dataset from theremote computing system 340 in order to perform the operation. Inanother example, a worker process 314 may be required to trigger theremote computing system 340 to perform an action before that workerprocess 314 can perform the operation. In another example, the workerprocess 314 can determine, from the operation assigned, the data thatmay be required by the remote computing systems 340 and/or from otheroperations previously performed.

In response to the trigger by the worker process 314 a, for example, aremote computing device, such as 340 c, can respond with at least atransition indicator to indicate the subsequent step to be performed.The remote computing system 340 c can also respond with relevant data,depending on the operation.

As the worker processes 314 perform the respective operations, theworker processes 314 will collect various datasets that are relevant tothe parameters of the notification(s) to be generated by the scalablecomputing system 110. The processor 112 can store the data gathered bythe worker process 314 into the database 320.

Reference will now be made to FIG. 4 for an example embodiment ofoperating the scalable computing system 110 for generating anotification related to a trading order for an example investmentaccount.

As will be described with reference to FIG. 4, an example notificationgenerator operation can include receiving parameters for thenotifications from at least one other operation defined by the actiondefinition. The notification generator operation can generate thenotifications based on the received parameters.

FIG. 4 is a state machine diagram 400 illustrating various operationsconducted by the scalable computing system 110 and the subsequentoperations following a specific state obtained at a prior operation. Thestate of each operation and the associated data obtained as a result ofthe operation are stored by the processor 112 into the database 320. Themanagement process 312 can track the state of each operation byreferring to the database 320.

At 410, an operation involves retrieving data related to positions andrecent activities of the account. The management process 312 candetermine that operation 410 involves triggering a remote computingsystem 340 for retrieving the position and recent activity data, and canselect a worker process 314 suitable for data retrieval, such as workerprocess 314 a. To retrieve the position and recent activity data, theworker process 314 a can trigger a communication with a remote computingsystem 340 with access to a database in which the position and recentactivity data is stored, such as remote computing system 340 a.

In response to the trigger generated by the worker process 314 a, theremote computing system 340 a can respond with the position and recentactivity data and a transition indicator to indicate the subsequentstep. As shown in FIG. 4, the transition indicator received from theremote computing system 340 a can be “success”, which indicates thatoperation 418 is then performed.

However, if the remote computing system 340 a does not respond to therequest from the worker process 314 a within a predefined time limit,the worker process 314 a can store in the database 320 an expired statefor the operation 410. The management process 312 can cause the workerprocess 314 a to restart the operation 410 at a later time.

Similar to operation 410, the management process 312 can determine thatoperation 418 involves triggering a remote computing system 340 forretrieving the trading attribute data, and can select a worker process314 suitable for data retrieval, such as worker process 314 b. In someembodiments, worker process 314 a can also be assigned to operation 418.For example, a different thread of the worker process 314 a can beassigned to operation 410 and operation 418, respectively. In anotherexample, the worker process 314 a can perform operations 410 and 418 insequence.

To retrieve the trading attribute data, the worker process 314 b cantrigger a communication with a remote computing system 340 with accessto a database in which the trading attribute data is stored, such asremote computing system 340 b. In some embodiments, both remotecomputing systems 340 a and 340 b can have access to both the tradingattribute data and the position and recent activity data. The workerprocess 314 b can trigger a communication with either of the remotecomputing systems 340 a or 340 b.

In response to the trigger generated by the worker process 314 b, theremote computing system 340 b can respond with the trading attributedata and a transition indicator to indicate the subsequent step. Asshown in FIG. 4, the transition indicator received from the remotecomputing system 340 b can be “success”, which indicates that operation422 is then performed.

Operation 422 involves retrieving investment profiles. For example, theinvestment profile can relate to a United States or Canadian market,various account types (e.g., individual retirement accounts (IRAs),tax-free savings accounts (TFSAs), registered retirement savings plans(RRSPs), 401k accounts, non-registered accounts, etc.), targetportfolios and/or fixed fee structures.

The management process 412 can determine that operation 422, similar tooperations 410 and 418, involves data retrieval and can assign anyworker process 314 suitable for data retrieval. Unlike operations 410and 418, operation 422 is associated with at least four differenttransition indicators, namely success, missing investment profile,undiscoverable foreign positions and placeholder investment profile.Each of these transition indicators indicates a different subsequentstep. As shown in FIG. 4, the subsequent operation when the operation422 returns the transition indicator indicating “success” is operation438, which involves ensuring the account is tradeable. However, when theoperation 422 returns the transition indicator indicating “missinginvestment profiles”, “undiscoverable foreign positions” or “placeholderinvestment profile”, the respective states 426, 430 and 434 are storedin the database 320. The management process 312 can then review thesestates 426, 430 and 434, and trigger the corresponding resolutions.

Continuing with operation 438, as shown in FIG. 4, each of subsequentoperations 440 (retrieve fee rates), 444 (retrieve pending withdrawals)and 448 (retrieve pricing) involves data retrieval. The managementprocess 412 can, therefore, assign worker processes 314 that aresuitable for operations 440, 444, and 448.

When the management process 312 determines that the operation 438 isassociated with a success state, the management process 312 can triggerthe assigned worker process 314 to perform operation 440. Similarly, themanagement process 312 can trigger the assigned worker process 314 toperform operation 444 when the operation 440 is associated with asuccess state.

When the transition indicator associated with the operation 448 is“success,” the management process 312 causes operation 456 to beperformed. When the transition indicator associated with the operation448 is “failure,” the operation 448 is associated with a missing quotesstate 452. The management process 312 can then trigger the necessaryresolution for addressing resolving the missing quotes state 452.

The operation 456 involves determining trades. The management process312 can determine that operation 456 involves analyzing the dataretrieved from at least one of the other operations 410, 418, 422, 438,440, 444 and 448 for determining the trades. As no further dataretrieval may be needed, the management process 312 can select a workerprocess, such as 314 f, which is designed to accommodate highcomputational speeds.

As shown in FIG. 4, operation 456 is associated with at least sixdifferent transition indicators, such as success, partial success, noorders, non-tradeable, bad withdrawal and optimization failed. When theworker process 314 assigned to the operation 456 returns the transitionindicator “success” or “partial success”, the subsequent operation is460 (to create orders). For the other transition indictors (e.g., noorders, non-tradeable, bad withdrawal and optimization failed), arespective state 472, 476, 480 and 484 are stored in the database 320.As with operations 422 and 448, the management process 312 can thenreview the states 472, 476, 480 and 484, and trigger the correspondingresolutions.

Referring now to FIG. 5, which is a state machine diagram 500 for anexample embodiment of operating the scalable computing system 110 forgenerating notifications related to a withdrawal service.

At 510, operation involves getting a withdrawal amount. When the workerprocess 314 assigned to operation 510 by the management process 312receives a transition indicator indicating the operation 510 is success,the management process 312 can cause operation 518 to be performed. Whenthe worker process 314 generates a cancelled transition indicator to themanagement process 312, the management process 312 can assign acancelled state 514 to the operation 510.

Operation 518 involves determining whether the cash has been generated.In the case that the transition indicator returned by the assignedworker process 314 is failure, the operation 518 is assigned the state522 “sleep until cash generated”. When the management process 312determines that the state 522 has been resolved, the management process312 causes operation 510 to be performed again.

When the transition indicator returned by the assigned worker process314 is success, operation 526 (adjust withdrawal cash amount by fee) isperformed, followed by operation 530 (funds transfer cash generated)when operation 526 is assigned a “success” transition indicator.

When the operation 530 is assigned a “success” transition indicator, themanagement process 312 assigns the operation 530 to a state 534, whichis to sleep until a withdrawal activity takes place. When the managementprocess 312 determines that the state 534 is associated with a “success”transition indicator, the management process 312 causes operation 538 toconfirm a withdrawal activity took place.

When operation 538 is associated with a “success” transition indicator,the management process 312 causes performance of operation 542 by theassigned worker process 314. Operation 542 involves completing the fundstransfer. However, if operation 538 is associated with a “failure”transition indicator, the management process 312 causes the performanceof operation 560 by the assigned worker process 314, which is to confirmthe funds transfer status. If operation 560 is assigned any one ofposted, accepted or pending states, the management process 312 canresume the state 534. Otherwise, if operation 560 is assigned the state“rejected” 564, the management process 312 can indicate that thewithdrawal process has failed.

Reference is now made to FIG. 6 for a description of an exampleapplication of the scalable computing system 110 for monitoring amanufacturing process. FIG. 6 shows an example state diagram 600.

At 610, the processor 112 receives supply parameters related to amanufacturing process. The management process 312 can determine thatoperation 610 involves triggering a remote computing system 340 forretrieving the supply parameters. To retrieve the supply parameters, theworker process 314 a can trigger a communication with a remote computingsystem 340 with access to a database in which the supply parameters isstored, such as remote computing system 340 a.

In response to the trigger generated by the worker process 314 a, theremote computing system 340 a can respond with the position and recentactivity data and a transition indicator to indicate the subsequentstep. As shown in FIG. 6, the transition indicator received from theremote computing system 340 a can be “success”, which indicates thatoperation 614 is then performed, or that the operation 610 is cancelled,resulting in state 612. The operation 610 can be cancelled when theprocessor 112 receives a cancellation signal or generates a cancellationsignal, and/or the worker process 314 a does not respond within apredefined time period, for example.

Similar to operation 610, the management process 312 can determine thatoperation 614 involves triggering a remote computing system 340 totrigger the retrieval of the raw materials, and can select a workerprocess 314 suitable for triggering the retrieval of the raw materials,such as worker process 314 b. Depending on the manufacturing process,the worker process 314 can trigger one or more robotic devices toautomatically retrieve the necessary raw materials from a storage areaor facility.

When the worker process 314 b returns a “success” transition indicator,the management process 312 can proceed to the next operation 618, namelymanufacturing.

However, if the worker process 314 b returns a “failure” transitionindicator, the management process 312 proceeds to an order raw materialsstate 616. The worker process 314 b can return the “failure” transitionindicator when there are insufficient raw materials in storage, forexample. When the order raw materials state 616 is successful, themanagement process 312 again triggers operation 614.

At 618, the management process 312 can trigger a remote computing system340 to trigger the manufacturing to occur. For example, a worker process314 c selected by the management process 312 can trigger an assemblyline to initiate the manufacturing process and/or one or more roboticdevices stationed at the assembly line to initiate the manufacturingprocess.

When the worker process 314 c returns a “success” transition indicator,the management process 312 proceeds to operation 620. At 620, themanagement process 312 selects a worker process, such as worker process314 d, to trigger a quality review of the result of the manufacturingprocess. For example, the worker process 314 d selected by themanagement process 312 can trigger one or more robotic devices toconduct the quality review. When worker process 314 d returns a“failure” transition indicator, such as an defective product or aproduct with features that do not satisfy a standard, the managementprocess 312 proceeds to a determine issue state 622. The determine issuestate 622 can involve a manual review and/or test of the product atissue and, if appropriate, repairing the product at issue to resolve theissue.

When the worker process 314 d returns a success transition indicator,the management process 312 proceeds to operation 624. At 624, themanagement process 312 can select a worker process, such as 314 e, totrigger the packaging of the products resulting at 620. The workerprocess 314 e can trigger the operation of robotic devices to performthe packaging, in some embodiments. When the worker process 314 dreturns a success transition indicator, the management process 312proceeds to 626.

At 250, the processor 112, upon detecting a predefined condition in eachindependent management process 312, transmits the notifications for theindependent management process 312 to a remote node 130 via the network150.

Referring again to FIG. 4, following a return of the transitionindicator “success” or “partial success”, the management process 312 canperform operation 460. When the operation 460 is associated with thestate “success”, the management process 312 can determine that thenotification generator operation is complete 464. That is, for theexample notification generator operation, the predefined condition isassociated with successfully creating an order and upon detecting thepredefined condition, the scalable computing system 110 can determinethat the predefined condition is satisfied. In this example, themanagement process 312 can generate notifications in the forms of tradeorders for transmission to the remote node 130, such as a brokerage.

In the case that no orders are generated, the operation 460 isassociated with a failed state 468 and no orders are generated. Themanagement process 312 may transmit a notification to the remote usernode 330, which may be a user interface, to indicate no orders have beengenerated.

Referring again to FIG. 5, once operation 542 is associated with a“success” transition indicator, the management process 312 generates anotification to indicate that the withdrawal process being monitored iscomplete 546. Following determining by the management process 312 thatthe operation 560 is assigned the state “rejected” 564, the managementprocess 312 can indicate that the withdrawal process has failed andgenerate a corresponding notification to indicate the failure to theremote note 130.

Referring again to FIG. 6, at 626, the management process 312 determineswhether the packaged product is ready to be shipped. If the product isready to be shipped, the management process 312 proceeds to 630. If theproduct is not ready to be shipped, the management process 312 proceedsto store the products in storage 628. At 630, the management process 312can trigger a worker process, such as 314 f, to trigger one or morerobotic devices for preparing the packaged products for shipping and togenerate a notification to the remote node 130 to indicate that theproduct has shipped. When the worker process 314 f returns a successtransition indicator, the management process 312 proceeds to a completestate 632.

It will be appreciated that numerous specific details are set forth inorder to provide a thorough understanding of the example embodimentsdescribed herein. It will be understood by those of ordinary skill inthe art that the embodiments described herein may be practiced withoutthese specific details. In other instances, well-known methods,procedures and components have not been described in detail so as not toobscure the embodiments described herein. Furthermore, this descriptionand the drawings are not to be considered as limiting the scope of theembodiments described herein in any way, but rather as merely describingthe implementation of the various embodiments described herein.

It should be noted that terms of degree such as “substantially”, “about”and “approximately” when used herein mean a reasonable amount ofdeviation of the modified term such that the end result is notsignificantly changed. These terms of degree should be construed asincluding a deviation of the modified term if this deviation would notnegate the meaning of the term it modifies.

In addition, as used herein, the wording “and/or” is intended torepresent an inclusive-or. That is, “X and/or Y” is intended to mean Xor Y or both, for example. As a further example, “X, Y, and/or Z” isintended to mean X or Y or Z or any combination thereof.

It should be noted that the term “coupled” used herein indicates thattwo elements can be directly coupled to one another or coupled to oneanother through one or more intermediate elements.

Unless the context requires otherwise, the word “include” as usedthroughout this specification, and variations thereof, such as“includes” and “including,” are to be construed in an open, inclusivesense, that is as “including, but not limited to.”

The embodiments of the systems and methods described herein may beimplemented in hardware or software, or a combination of both. Theseembodiments may be implemented in computer programs executing onprogrammable computers, each computer including at least one processor,a data storage system (including volatile memory or non-volatile memoryor other data storage elements or a combination thereof), and at leastone communication interface. For example and without limitation, theprogrammable computers (referred to as computing devices) may be aserver, network appliance, embedded device, computer expansion module, apersonal computer, laptop, personal data assistant, cellular telephone,smart-phone device, tablet computer, a wireless device or any othercomputing device capable of being configured to carry out the methodsdescribed herein.

In some embodiments, the communication interface may be a networkcommunication interface. In embodiments in which elements are combined,the communication interface may be a software communication interface,such as those for inter-process communication (IPC). In still otherembodiments, there may be a combination of communication interfacesimplemented as hardware, software, and combination thereof.

Program code may be applied to input data to perform the functionsdescribed herein and to generate output information. The outputinformation is applied to one or more output devices, in known fashion.

Each program may be implemented in a high level procedural or objectoriented programming and/or scripting language, or both, to communicatewith a computer system. However, the programs may be implemented inassembly or machine language, if desired. In any case, the language maybe a compiled or interpreted language. Each such computer program may bestored on a storage media or a device (e.g. ROM, magnetic disk, opticaldisc) readable by a general or special purpose programmable computer,for configuring and operating the computer when the storage media ordevice is read by the computer to perform the procedures describedherein. Embodiments of the system may also be considered to beimplemented as a non-transitory computer-readable storage medium,configured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner to perform the functions described herein.

Furthermore, the system, processes and methods of the describedembodiments are capable of being distributed in a computer programproduct comprising a computer readable medium that bears computer usableinstructions for one or more processors. The medium may be provided invarious forms, including one or more diskettes, compact disks, tapes,chips, wireline transmissions, satellite transmissions, internettransmission or downloadings, magnetic and electronic storage media,digital and analog signals, and the like. The computer useableinstructions may also be in various forms, including compiled andnon-compiled code.

Various embodiments have been described herein by way of example only.Various modification and variations may be made to these exampleembodiments without departing from the spirit and scope of theinvention, which is limited only by the appended claims.

We claim:
 1. A scalable computing system for generating one or morenotifications for each of a plurality of accounts, the scalablecomputing system comprising: a database for storing: an actiondefinition defining one or more operations executable to determine atleast one parameter of one or more actions; and a processor incommunication with the database, the processor being operable to:instantiate an independent management process for the action definitionof each account of the plurality of accounts managed by the scalablecomputing system, wherein each management process is configured to:monitor a state of each operation defined in the action definition;identify a worker process associated with the management process toperform each operation based on the respective state of the operation;and assign the identified worker process to perform the respectiveoperation; and upon detecting a predefined condition in each independentmanagement process, transmit, via a network to a remote node, the one ormore notifications for the respective independent management process. 2.The scalable computing system of claim 1, wherein the management processis configured to: determine the state of each operation defined in therespective action definition; and identify an operation with anincomplete status.
 3. The scalable computing system of claim 1, whereinthe management process is configured to: determine a load parameter ofeach operation, the load parameter indicating a processing capacityrequired for performing the respective operation; identify one or moreworker processes capable of satisfying the load parameter; and select aworker process from the identified one or more worker processes forperforming the operation.
 4. The scalable computing system of claim 3,wherein the management process is configured to: determine a machinetype associated with the load parameter; and identify the one or moreworker processes available at a machine associated with the machinetype.
 5. The scalable computing system of claim 3, wherein themanagement process is configured to: determine whether an operatingcapacity of the identified one or more worker processes exceeds acapacity threshold; and in response to determining the operatingcapacity exceeds the capacity threshold, delay the performance of theoperation until the operating capacity does not exceed the capacitythreshold.
 6. The scalable computing system of claim 5, wherein, todelay the performance of the operation, the management process isconfigured to: assign a wait state to that operation.
 7. The scalablecomputing system of claim 1, wherein: the one or more operations definedby the action definition comprises an notification generator operation;and the management process is configured to identify the worker processto perform the notification generator operation, wherein thenotification generator operation comprises: receiving one or moreparameters for the one or more notifications from at least one otheroperation defined by the action definition; and generating the one ormore notifications based on the one or more parameters.
 8. The scalablecomputing system of claim 1, wherein: the one or more operations definedby the action definition comprises at least one of a risk profiledetermination operation, a portfolio determination operation, a positiondetermination operation, a fee determination operation, an accountstatus determination operation, and an order submission operation. 9.The scalable computing system of claim 1, wherein the processor isoperable to instantiate two or more management processes, eachmanagement process being associated with a different set of workerprocesses.
 10. The scalable computing system of claim 1, wherein themanagement process is associated with one or more worker processesselectable by the management process for performing a respectiveoperation defined by the action definition.
 11. The scalable computingsystem of claim 1, wherein the independent management process isconfigured to implement a state machine for the respective actiondefinition.
 12. A method of operating a scalable computing system togenerate one or more notifications for each of a plurality of accounts,the method comprising: operating a processor to instantiate anindependent management process for an action definition of each accountof the plurality of accounts managed by a scalable computing system, theaction definition defining one or more operations executable todetermine at least one parameter of one or more actions, wherein eachmanagement process is configured to: monitor a state of each operationdefined in the action definition; identify a worker process associatedwith the management process to perform each operation based on therespective state of the operation; and assign the identified workerprocess to perform the respective operation; and operating the processorto, upon detecting a predefined condition in each independent managementprocess, transmit, via a network to a remote node, the one or morenotifications for the respective independent management process.
 13. Themethod of claim 12, wherein the management process is configured to:determine the state of each operation defined in the respective actiondefinition; and identify an operation with an incomplete status.
 14. Themethod of claim 12, wherein the management process is configured to:determine a load parameter of each operation, the load parameterindicating a processing capacity required for performing the respectiveoperation; identify one or more worker processes capable of satisfyingthe load parameter; and select a worker process from the identified oneor more worker processes for performing the operation.
 15. The method ofclaim 14, wherein the management process is configured to: determine amachine type associated with the load parameter; and identify the one ormore worker processes available at a machine associated with the machinetype.
 16. The method of claim 14, wherein the management process isconfigured to: determine whether an operating capacity of the identifiedone or more worker processes exceeds a capacity threshold; and inresponse to determining the operating capacity exceeds the capacitythreshold, delay the performance of the operation until the operatingcapacity does not exceed the capacity threshold.
 17. The method of claim16, wherein, to delay the performance of the operation, the managementprocess is configured to: assign a wait state to that operation.
 18. Themethod of claim 12, wherein: the one or more operations defined by theaction definition comprises an notification generator operation; and themanagement process is configured to identify the worker process toperform the notification generator operation, wherein the notificationgenerator operation comprises: receiving one or more parameters for theone or more notifications from at least one other operation defined bythe action definition; and generating the one or more notificationsbased on the one or more parameters.
 19. The method of claim 12,wherein: the one or more operations defined by the action definitioncomprises at least one of a risk profile determination operation, aportfolio determination operation, a position determination operation, afee determination operation, an account status determination operation,and an order submission operation.
 20. The method of claim 12, furthercomprising operating the processor to instantiate two or more managementprocesses, each management process being associated with a different setof worker processes.
 21. The method of claim 12, wherein the managementprocess is associated with one or more worker processes selectable bythe management process for performing a respective operation defined bythe action definition.
 22. The method of claim 12, wherein theindependent management process is configured to implement a statemachine for the respective action definition.
 23. The method of claim12, further comprising storing the action definition in a databaseaccessible by the processor.